47 results
Patient altruism at the end of life: A scoping review
- Anca-Cristina Sterie, Gian Domenico Borasio, Michael J. Deml, Claudia Gamondi, Ralf J. Jox, Philip Larkin, Alexia Trombert, Eve Rubli Truchard, Mathieu Bernard
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- Journal:
- Palliative & Supportive Care , First View
- Published online by Cambridge University Press:
- 12 April 2024, pp. 1-13
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Objectives
The concept of altruism is evidenced in various disciplines but remains understudied in end-of-life (EOL) contexts. Patients at the EOL are often seen as passive recipients of care, whereas the altruism of professionals and families receives more research and clinical attention. Our aim was to summarize the state of the scientific literature concerning the concept of patient altruism in EOL contexts.
MethodsIn May 2023, we searched 11 databases for scientific literature on patient altruism in EOL contexts in consultation with a health information specialist. The scoping review is reported using the PRISMA checklist for scoping reviews. We used a data charting form to deductively extract data from the selected articles and then mapped data into 4 themes related to our research questions: how authors describe and employ the concept of patient altruism; expressions of patient altruism; consequences of patients’ altruistic acts; and possible interventions fostering patient altruism.
ResultsExcluding duplicates, 2893 articles were retrieved; 33 were included in the final review. Altruism was generally considered as an act or intention oriented toward the benefit of a specific (known) or non-specific (generic) recipient. Patients expressed altruism through care and support, decisions to withhold treatment or actively hasten death, and engagement in advance care planning. Consequences of altruism were categorized in patient-centered (contribution to meaning in life and quality of life), non-patient-centered (leaving a positive impact and saving money), and negative consequences (generating feelings of guilt, exposing individuals with low self-esteem). Interventions to encourage altruism comprised specific interventions, providing opportunities to plan for future care, and recognizing and respecting the patients’ altruistic motivations.
Significance of resultsWe identified heterogeneous and limited research conceptualization of patient altruism and its operationalization in palliative care settings. A deeper conceptual, empirical, and theoretical exploration of patient altruism in EOL is necessary.
15 Exploratory Factor Analysis of Cognitive and Positive Valence Measures for the RDoC
- Emily T Sturm, John R Duffy, Anastasia G Sares, Andrea Mendez-Colmenares, Lauren Sarabia, Eve Delao, Max Henneke, Raana Manavi, Donald C Rojas, Jason R Tregellas, Jared W Young, Michael L Thomas
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 698-699
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Objective:
As part of the Research Domain Criteria (RDoC) initiative, the NIMH seeks to improve experimental measures of cognitive and positive valence systems for use in intervention research. However, many RDoC tasks have not been psychometrically evaluated as a battery of measures. Our aim was to examine the factor structure of 7 such tasks chosen for their relevance to schizophrenia and other forms of serious mental illness. These include the n-back, Sternberg, and self-ordered pointing tasks (measures of the RDoC cognitive systems working memory construct); flanker and continuous performance tasks (measures of the RDoC cognitive systems cognitive control construct); and probabilistic learning and effort expenditure for reward tasks (measures of reward learning and reward valuation constructs).
Participants and Methods:The sample comprised 286 cognitively healthy participants who completed novel versions of all 7 tasks via an online recruitment platform, Prolific, in the summer of 2022. The mean age of participants was 38.6 years (SD = 14.5, range 18-74), 52% identified as female, and stratified recruitment ensured an ethnoracially diverse sample. Excluding time for instructions and practice, each task lasted approximately 6 minutes. Task order was randomized. We estimated optimal scores from each task including signal detection d-prime measures for the n-back, Sternberg, and continuous performance task, mean accuracy for the flanker task, win-stay to win-shift ratio for the probabilistic learning task, and trials completed for the effort expenditure for reward task. We used parallel analysis and a scree plot to determine the number of latent factors measured by the 7 task scores. Exploratory factor analysis with oblimin (oblique) rotation was used to examine the factor loading matrix.
Results:The scree plot and parallel analyses of the 7 task scores suggested three primary factors. The flanker and continuous performance task both strongly loaded onto the first factor, suggesting that these measures are strong indicators of cognitive control. The n-back, Sternberg, and self-ordered pointing tasks strongly loaded onto the second factor, suggesting that these measures are strong indicators of working memory. The probabilistic learning task solely loaded onto the third factor, suggesting that it is an independent indicator of reinforcement learning. Finally, the effort expenditure for reward task modestly loaded onto the second but not the first and third factors, suggesting that effort is most strongly related to working memory.
Conclusions:Our aim was to examine the factor structure of 7 RDoC tasks. Results support the RDoC suggestion of independent cognitive control, working memory, and reinforcement learning. However, effort is a factorially complex construct that is not uniquely or even most strongly related to positive valance. Thus, there is reason to believe that the use of at least 6 of these tasks are appropriate measures of constructs such as working memory, reinforcement learning and cognitive control.
70 Comparison of MCCB Autocorrelations Between Schizophrenia and Healthy Comparison Populations
- Max Henneke, Emily T. Sturm, John R. Duffy, Anastasia Sares, Andrea Mendez-Colmenares, Lauren Sarabia, Eve Delao, Tessa Mitchell, Raana Manavi, Michael L. Thomas
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 854-855
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Objective:
Deficits in cognitive ability are common among patients with schizophrenia. The MATRICS Consensus Cognitive Battery (MCCB) was designed to assess cognitive ability in studies of patients diagnosed with schizophrenia and has demonstrated high test-retest reliability with minimal practice effects, even in multi-site trials. However, given the motivational challenges associated with schizophrenia, it is unknown whether performance on MCCB tasks affects performance at later stages of testing. The goal of this study was to determine whether there are differences between people with and without schizophrenia in how their performance on individual MCCB tasks influences their performance throughout the battery.
Participants and Methods:The sample comprised 92 total participants including 49 cognitively healthy comparison participants and 43 outpatients diagnosed with schizophrenia. The mean age of participants was 44.2 years (SD = 12.0, range 21–69) and 61% identified as male. The Trail Making Test, Brief Assessment of Cognition in Schizophrenia, Hopkins Verbal Learning Test – Revised, Letter-Number Span, and Category Fluency from the MCCB were administered in the same order at 2 different sites and studies from 2016–2022. The autocorrelation between t-scores for task scores within each participant was computed and then compared between control and outpatient participants to determine if there are differences between groups. Group mean t-scores for each task were also compared between groups.
Results:We found no significant difference in autocorrelations across MCCB tasks between healthy comparison participants and outpatients. However, mean performance in all tasks was lower for the outpatient group than for the healthy comparison group. None of the tasks used stood out as having significantly lower mean scores than other tasks for either group.
Conclusions:Our findings suggest that performance on individual MCCB tasks do not affect performance throughout the battery differently between the healthy comparison group and outpatients. This suggests that participants with schizophrenia are not particularly reactive to past performance on MCCB tasks. Additionally, this finding further supports use of the MCCB in this population. Further research is needed to determine whether subgroups of patients and/or different batteries of measures show different patterns of reactivity.
Development, implementation, and dissemination of operational innovations across the trial innovation network
- Marisha E. Palm, Terri L. Edwards, Cortney Wieber, Marie T. Kay, Eve Marion, Leslie Boone, Angeline Nanni, Michelle Jones, Eilene Pham, Meghan Hildreth, Karen Lane, Nichol McBee, Daniel K. Benjamin, Jr, Gordon R. Bernard, J. Michael Dean, Jamie P. Dwyer, Daniel E. Ford, Daniel F. Hanley, Paul A. Harris, Consuelo H. Wilkins, Harry P. Selker
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- Journal:
- Journal of Clinical and Translational Science / Volume 7 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 20 October 2023, e251
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Improving the quality and conduct of multi-center clinical trials is essential to the generation of generalizable knowledge about the safety and efficacy of healthcare treatments. Despite significant effort and expense, many clinical trials are unsuccessful. The National Center for Advancing Translational Science launched the Trial Innovation Network to address critical roadblocks in multi-center trials by leveraging existing infrastructure and developing operational innovations. We provide an overview of the roadblocks that led to opportunities for operational innovation, our work to develop, define, and map innovations across the network, and how we implemented and disseminated mature innovations.
Approaches for enhancing the informativeness and quality of clinical trials: Innovations and principles for implementing multicenter trials from the Trial Innovation Network
- Karen Lane, Marisha E. Palm, Eve Marion, Marie T. Kay, Dixie Thompson, Mary Stroud, Helen Boyle, Shannon Hillery, Angeline Nanni, Meghan Hildreth, Sarah Nelson, Jeri S. Burr, Terri Edwards, Lori Poole, Salina P. Waddy, Sarah E. Dunsmore, Paul Harris, Consuelo Wilkins, Gordon R. Bernard, J. Michael Dean, Jamie Dwyer, Daniel K. Benjamin, Jr., Harry P. Selker, Daniel F. Hanley, Daniel E. Ford
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- Journal:
- Journal of Clinical and Translational Science / Volume 7 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 25 May 2023, e131
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One challenge for multisite clinical trials is ensuring that the conditions of an informative trial are incorporated into all aspects of trial planning and execution. The multicenter model can provide the potential for a more informative environment, but it can also place a trial at risk of becoming uninformative due to lack of rigor, quality control, or effective recruitment, resulting in premature discontinuation and/or non-publication. Key factors that support informativeness are having the right team and resources during study planning and implementation and adequate funding to support performance activities. This communication draws on the experience of the National Center for Advancing Translational Science (NCATS) Trial Innovation Network (TIN) to develop approaches for enhancing the informativeness of clinical trials. We distilled this information into three principles: (1) assemble a diverse team, (2) leverage existing processes and systems, and (3) carefully consider budgets and contracts. The TIN, comprised of NCATS, three Trial Innovation Centers, a Recruitment Innovation Center, and 60+ CTSA Program hubs, provides resources to investigators who are proposing multicenter collaborations. In addition to sharing principles that support the informativeness of clinical trials, we highlight TIN-developed resources relevant for multicenter trial initiation and conduct.
Context-Dependent Learning of Linguistic Disjunction
- Masoud JASBI, Akshay JAGGI, Eve V. CLARK, Michael C. FRANK
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- Journal of Child Language / Volume 51 / Issue 1 / January 2024
- Published online by Cambridge University Press:
- 10 November 2022, pp. 1-36
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What are the constraints, cues, and mechanisms that help learners create successful word-meaning mappings? This study takes up linguistic disjunction and looks at cues and mechanisms that can help children learn the meaning of or. We first used a large corpus of parent-child interactions to collect statistics on or uses. Children started producing or between 18-30 months and by 42 months, their rate of production reached a plateau. Second, we annotated for the interpretation of disjunction in child-directed speech. Parents used or mostly as exclusive disjunction, typically accompanied by rise-fall intonation and logically inconsistent disjuncts. But when these two cues were absent, disjunction was generally not exclusive. Our computational modeling suggests that an ideal learner could successfully interpret an English disjunction (as exclusive or not) by mapping forms to meanings after partitioning the input according to the intonational and logical cues available in child-directed speech.
Relationships Among Cleaning, Environmental DNA, and Healthcare-Associated Infections in a New Evidence-Based Design Hospital
- Emil Lesho, Philip Carling, Eve Hosford, Ana Ong, Erik Snesrud, Michael Sparks, Fatma Onmus-Leone, Nicole Dzialowy, Susan Fraser, Yoon Kwak, Sonia Miller, Uzo Chukwuma, Michael Julius, Patrick McGann, Robert Clifford
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 36 / Issue 10 / October 2015
- Published online by Cambridge University Press:
- 08 July 2015, pp. 1130-1138
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- October 2015
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OBJECTIVE
Hospital environments influence healthcare-associated infection (HAI) patterns, but the role of evidenced-based design (EBD) and residual bacterial DNA (previously thought to be clinically inert) remain incompletely understood.
METHODSIn a newly built EBD hospital, we used culture-based and culture-free (molecular) assays, pulsed-field gel electrophoresis (PFGE), and whole-genome sequencing (WGS) to determine: (1) patterns of environmental contamination with target organisms (TOs) and multidrug-resistant (MDR) target organisms (MDR-TOs); (2) genetic relatedness between environmentally isolated MDR-TO and those from HAIs; and (3) correlation between surface contamination and HAIs.
RESULTSA total of 1,273 high-touch surfaces were swabbed before and after terminal cleaning during 77 room visits. Of the 2,546 paired swabs, 47% had cultivable biomaterial and 42% had PCR-amplifiable DNA. The ratios of TOs detected to surfaces assayed were 85 per 1,273 for the culture-based method and 106 per 1,273 for the PCR-based method. Sinks, toilet rails, and bedside tables most frequently harbored biomaterial. Although cleaned surfaces were less likely to have cultivable TOs than precleaned surfaces, they were not less likely to harbor bacterial DNA. The rate of MDR-TOs to surfaces swabbed was 0.1% (3/2546). Although environmental MDR-TOs and MDR-TOs from HAIs were genetically related by PFGE, WGS revealed that they were unrelated. Environmental levels of cultivable Enterococcus spp. and E. coli DNA were positively correlated with infection incidences (P<.04 and P<.005, respectively).
CONCLUSIONMDR-TOs were rarely detected during surveillance and were not implicated in HAIs. The roles of environmental DNA and EBD, particularly with respect to water-associated fixtures or the potential suppression of cultivable environmental MDR-TOs, warrant multicenter investigations.
Infect Control Hosp Epidemiol 2015;36(10):1130–1138
Contributors
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- By Fiona Jenkins, Mark Nolan, Kim Rubenstein, Elisa Arcioni, Peter Balint, Sharon Bessell, Vito Breda, Ben Golder, Diana Grace, Rishi Gulati, Susan Harris Rimmer, George Hoa’au, Tamás Hoffmann, Susan Kneebone, Eve Lester, Pablo Cristóbal Jiménez Lobeira, Simon Marsden, Christopher Michaelsen, Rebecca Monson, Joshua Neoh, Valeria Ottonelli, Michael Platow, Thomas Pogge, Donald R. Rothwell, Mohammad Shahabuddin, Michael Smithson, Peter J. Spiro, Rayner Thwaites, Tiziana Torresi, Jo-Anne Weinman, Asmi J. Wood, Matthew Zagor
- Edited by Fiona Jenkins, Australian National University, Canberra, Mark Nolan, Australian National University, Canberra, Kim Rubenstein, Australian National University, Canberra
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- Allegiance and Identity in a Globalised World
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- 05 November 2014
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- 06 November 2014, pp viii-xvi
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About the authors
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp xviii-xxii
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7 - Multiple test results
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 165-208
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Summary
Even though the diagnostic radiologist examines black-and-white images, the information that is derived from the images is hardly ever black-and-white.
M.G. Myriam HuninkIntroduction
In the previous chapters we focused on dichotomous test results, e.g., fecal occult blood is either present or absent. Test results can conveniently be dichotomized, and thinking in terms of dichotomous test results is generally helpful. Distinguishing patients with and without the target disease is useful for the purpose of subsequent decision making because most medical actions are dichotomous. In reality, however, most test results have more than two possible outcomes. Test results can be categorical, ordinal, or continuous. For example, categories of a diagnostic imaging test may be defined by key findings on the images. These categories may be ordered (intuitively) according to the observer’s confidence in the diagnosis, based on the findings. As an example, abnormalities seen on mammography are commonly reported as definitely malignant, probably malignant, possibly malignant, probably benign, or definitely benign. As we shall see later in this chapter, it makes sense to order the categories (explicitly) according to increasing likelihood ratio (LR). Some test results are inherently ordinal, e.g., the five categories of a Papanicolaou smear (test for cervical cancer) are ordinal. Results of biochemical tests are usually given on a continuous scale, which may be reduced to an ordinal scale by grouping the test results. Thus, a test result on a continuous scale can be considered a result on an ordinal scale with an infinite number of very narrow categories. Scores from prediction models are on an ordinal scale if there are a finite number of possible scores, and on a continuous scale if there are an infinite number of scores. When test results are categorical, ordinal, or continuous, we have to consider many test results Ri, where i can be any value from two (the case we have considered in Chapter 5 and Chapter 6, T+ and T−) up to any number of categories. Interpretation of a test result on an ordinal scale can be considered a generalization of the situation of dichotomous test results.
2 - Managing uncertainty
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 29-52
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Summary
Much of medical training consists of learning to cope with pervasive uncertainty and with the limits of medical knowledge. Making serious clinical decisions on the basis of conflicting, incomplete, and untimely data is routine.
J.D. McCueIntroduction
Much of clinical medicine and health care involves uncertainties: some reducible, but some irreducible despite our best efforts and tests. Better decisions will be made if we are open and honest about these uncertainties, and develop skills in estimating, communicating, and working with such uncertainties. What types of uncertainty exist? Consider the following example.
Needlestick injury:
It has been a hard week. It is time to go home when you are called to yet another heroin overdose: a young woman has been found unconscious outside your clinic. After giving intravenous (IV) naloxone (which reverses the effects of heroin), you are accidentally jabbed by the needle. After her recovery, despite your reassurances, the young woman flees for fear of the police. As the mêlée settles, the dread of human immunodeficiency virus (HIV) infection begins to develop. You talk to the senior doctor about what you should do. She is very sympathetic, and begins to tell you about the risks and management. The good news is that, even if the patient was HIV-positive, a needlestick injury rarely leads to HIV infection (about 3 per 1000). And if she was HIV-positive then a basic two-drug regime of antivirals such as zidovudine (AZT) plus lamivudine are likely to be able to prevent most infections (perhaps 80%).
Unfortunately, the HIV status of the young woman who had overdosed is unknown. Since she was not a patient of your clinic, you are uncertain about whether she is infected, but think that it is possible since she is an IV drug user. The Centers for Disease Control and Prevention (CDC) guidelines (1) suggest: ‘If the exposure source is unknown, use of post-exposure prophylaxis should be decided on a case-by-case basis. Consider the severity of exposure and the epidemiologic likelihood of HIV.’ What do you do?
Dedication
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
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- 16 October 2014, pp v-vi
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11 - Estimation, calibration, and validation
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
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- 16 October 2014, pp 334-355
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Summary
Essentially, all models are wrong, but some are useful.
George E. P. BoxIntroduction
As discussed in Chapter 8, ‘good decision analyses depend on both the veracity of the decision model and the validity of the individual data elements.’ The validity of each individual data element relies on the comprehensiveness of the literature search for the best and most appropriate study or studies, criteria for selecting the source studies, the design of the study or studies, and methods for synthesizing the data from multiple sources. Nonetheless, Sir Michael David Rawlins avers that ‘Decision makers have to incorporate judgements, as part of their appraisal of the evidence, in reaching their conclusions. Such judgements relate to the extent to which each of the components of the evidence base is “fit for purpose.” Is it reliable?’(1) Because the integration of a multitude of these ‘best available’ data elements forms the basis for model results, some individuals refer to decision analyses as black boxes, so this last question applies particularly to the overall model predictions. Consequently, assessing model validity becomes paramount. However, prior to assessing model validity, model construction requires attention to parameter estimation and model calibration. This chapter focuses on parameter estimation, calibration, and validation in the context of Markov and, more generally, state-transition models (Chapter 10) in which recurrent events may occur over an extended period of time. The process of parameter estimation, calibration, and validation is iterative: it involves both adjustment of the data to fit the model and adjustment of the model to fit the data.
Parameter estimation
Survival analysis involves determining the probability that an event such as death or disease progression will occur over time. The events modeled in survival analysis are called ‘failure’ events, because once they occur, they cannot occur again. ‘Survival’ is the absence of the failure event. The failure event may be death, or it may be death combined with a non-fatal outcome such as developing cancer or having a heart attack, in which case the absence of the event is referred to as event-free survival. Commonly used methods for survival analysis include life-table analysis, Kaplan–Meier product limit estimates, and Cox proportional hazards models. A survival curve plots the probability of being alive over time (Figure 11.1).
3 - Choosing the best treatment
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 53-77
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Summary
Firstly, do no (net) harm.
(adapted from) HippocratesIntroduction
Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma.
Benjamin Franklin and smallpox
Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1):
Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.
5 - Interpreting diagnostic information
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 118-144
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Summary
The interpretation of new information depends on what was already known about the patient.
Harold SoxDiagnostic information and probability revision
Physicians have at their disposal an enormous variety of diagnostic information to guide them in decision making. Diagnostic information comes from talking to the patient (symptoms, such as pain, nausea, and breathlessness), examining the patient (signs, such as abdominal tenderness, fever, and blood pressure), and from diagnostic tests (such as blood tests, X-rays, and electrocardiograms (ECGs)) and screening tests (such as Papanicolaou smears for cervical cancer or cholesterol measurements).
Physicians are not the only ones that have to interpret diagnostic information. Public policy makers in health care are equally concerned with understanding the performance of diagnostic tests. If, for example, a policy maker is considering a screening program for lung cancer, he/she will need to understand the performance of the diagnostic tests that can detect lung cancer in an early phase of the disease. In public policy making, other types of ‘diagnostic tests’ may also be relevant. For example, a survey with a questionnaire in a population sample can be considered analogous to a diagnostic test. And performing a trial to determine the efficacy of a treatment is in fact a ‘test’ with the goal of getting more information about that treatment.
list of Abbreviations
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
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- 16 October 2014, pp xvi-xvii
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6 - Deciding when to test
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 145-164
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Summary
Before ordering a test ask: What will you do if the test is positive? What will you do if the test is negative? If the answers are the same, then don’t do the test.
Poster in an Emergency DepartmentIntroduction
In the previous chapter we looked at how to interpret diagnostic information such as symptoms, signs, and diagnostic tests. Now we need to consider when such information is helpful in decision making. Even if they reduce uncertainty, tests are not always helpful. If used inappropriately to guide a decision, a test may mislead more than it leads. In general, performing a test to gain additional information is worthwhile only if two conditions hold: (1) at least one decision would change given some test result, and (2) the risk to the patient associated with the test is less than the expected benefit that would be gained from the subsequent change in decision. These conditions are most likely to be fulfilled when we are confronted with intermediate probabilities of the target disease, that is, when we are in a diagnostic ‘gray zone.’ Tests are least likely to be helpful either when we are so certain a patient has the target disease that the negative result of an imperfect test would not dissuade us from treating, or, conversely, when we are so certain that the patient does not have the target disease that a positive result of an imperfect test would not persuade us to treat. These concepts are illustrated in Figure 6.1, which divides the probability of a disease into three ranges:
do not treat (for the target disease) and do not test, because even a positive test would not persuade us to treat;
test, because the test will help with treatment decisions or with follow-up; and
treat and do not test, because even a negative test would not dissuade us from treating.
Treat implies patient management as if disease is present and may imply initiating medical therapy, performing a therapeutic procedure, advising a lifestyle or other adjuvant intervention, or a combination of these. Do not treat implies patient management as if disease is absent and usually means risk factor management, lifestyle advice, self-care and/or watchful waiting.
1 - Elements of decision making in health care
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
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- 16 October 2014, pp 1-28
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Summary
And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down and die between them: I must do something.
Samuel JohnsonIntroduction
How are decisions made in practice, and can we improve the process? Decisions in health care can be particularly awkward, involving a complex web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. It is not surprising that there is often considerable disagreement about the best course of action. One of the authors of this book tells the following story (1):
Being a cardiovascular radiologist, I regularly attend the vascular rounds at the University Hospital. It’s an interesting conference: the Professor of Vascular Surgery really loves academic discussions and each case gets a lot of attention. The conference goes on for hours. The clinical fellows complain, of course, and it sure keeps me from my regular work. But it’s one of the few conferences that I attend where there is a real discussion of the risks, benefits, and costs of the management options. Even patient preferences are sometimes (albeit rarely) considered.
And yet, I find there is something disturbing about the conference. The discussions always seem to go along the same lines. Doctor R. advocates treatment X because he recently read a paper that reported wonderful results; Doctor S. counters that treatment X has a substantial risk associated with it, as was shown in another paper published last year in the world’s highest-ranking journal in the field; and Doctor T. says that given the current limited health-care budget maybe we should consider a less expensive alternative or no treatment at all. They talk around in circles for ten to 15 minutes, each doctor reiterating his or her opinion. The professor, realizing that his fellows are getting irritated, finally stops the discussion. Practical chores are waiting; there are patients to be cared for. And so the professor concludes: ‘All right. We will offer the patient treatment X.’ About 30% of those involved in the decision-making process nod their heads in agreement; another 30% start bringing up objections which get stifled quickly by the fellows who really do not want an encore, and the remaining 40% are either too tired or too flabbergasted to respond, or are more concerned about another objective, namely their job security.
Contents
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Book:
- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp vii-vii
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8 - Finding and summarizing the evidence
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Book:
- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp 209-236
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- Export citation
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Summary
It is surely a great criticism of our profession that we have not organized a critical summary, by specialty or subspecialty, adapted periodically, of all relevant randomized controlled trials.
Archie CochraneIntroduction
Good decision analyses depend on both the veracity of the decision model and on the validity of the individual data elements. These elements may include probabilities (such as the pre-test probabilities, the sensitivity and specificity of diagnostic tests, the probability of an adverse event, and so on), estimates of effectiveness of interventions (such as the relative risk reduction), and the valuation of outcomes (such as quality of life, utilities, and costs). Often we lack the information needed for a confident assessment of these elements. Decision analysis, by structuring a decision problem, makes these gaps in knowledge apparent. Sensitivity analysis on these ‘soft’ numbers will also give us insight into which of these knowledge gaps is most likely to affect our decisions. These same gaps exist in less systematic decision making as well, but there is no convenient way to determine how our decisions should be affected. In this chapter we shall cover the basic methods for finding the best estimate for each of the different elements that may be included in a formal decision analysis or in less systematic decision making.
Sometimes, but not as often as one would like, the estimates one is looking for can be inferred from a published study or from a series of cases that someone has reported in the literature or recorded in a data bank. This is generally considered the most satisfactory way of assessing a probability, because it involves the use of quantitative evidence. Often we will have a choice of data sources, so it is useful to have some ‘rules’ to guide the choice of possible estimates. One helpful concept is the ‘hierarchy of evidence’ (see www.cebm.net) which explicitly ranks the available evidence; ‘perfect’ data will rarely be available, but we need to know how to choose the best from the available imperfect data. This choice will also need to be tempered by the practicalities and purpose of each decision analysis: what is feasible will differ with a range from the urgent individual patient decision to a national policy decision to fund an expensive new procedure.